Diretrizes para classificação automática de músicas cristãs: aplicação do Bert na identificação de adoração

Abstract

The study presented in this dissertation explores the application of advanced Natural Language Processing (NLP) algorithms to examine the lyrics of brazilian Christian songs. Utilizing the BERT model (Bidirectional Encoder Representations from Transformers), developed by Devlin et al. in 2019, this research aims to decipher linguistic and thematic patterns in the compositions, seeking to understand how these reflect beliefs, values, and religious identity in the brazilian context. Christian music is a vital component of brazilian culture, serving as an expression of faith and a way for the construction of both communal and individual identity. This study particularly focuses on sentiment analysis and the identification of worship themes, using BERT to analyze the complexity and richness of the lyrical content of the songs. The dissertation details the methodology employed in the data collection and analysis, highlighting the transformative role of BERT in the contextual understanding of texts. By combining quantitative and qualitative techniques, the research not only provides insights into the evolution of brazilian Christian music but also demonstrates the potential of using NLP models in the detailed study of musical texts. This work is significant for the fields of music studies, religion, and information technology, offering a new perspective on how technology can be utilized to deepen the understanding of contemporary cultural and spiritual dynamics.